Curriculum and Course Descriptions

The MS in Data Analytics degree requires 30-semester units consisting of

5 core courses (15 units)
3 elective (9 units)
2 culminating experience courses (6 units)

Each course consists of 3-semester units.

Remedial Courses

These courses aim to improve foundational data science skills allowing students to catch up and meet the necessary program standards.

DATA 200 - Computational Programming for Data Analytics
- Computational thinking and problem solving; data structures, algorithms, iterations, functions and other core concepts; standard libraries; functional programming and object-oriented programming; applications to data processing, data analytics, data visualization and database manipulation.
  
  Prerequisites: Admission into MS in Data Analytics and graduate advisor consent.
DATA 201 - Database Technologies for Data Analytics
- File organization and storage structure, database system architecture, entity relationship model, normalization techniques, SQL, relational algebra, storage organization, query processing and concurrent control.
  
  Prerequisites: Admission into MS in Data Analytics and graduate advisor consent.
DATA 202 - Mathematics for Applied Data Science
- Mathematical and statistical methods for applied data science; selected topics from probability, descriptive statistics, linear algebra and calculus principles; programming for numerical implementation of mathematical and statistical methods.
  
  Prerequisites: Admission into MS in Data Analytics and graduate advisor consent.

Waivers for Remedial Courses

Core Courses (5 courses, 15 units)

The program's core courses, totaling 15 units, lay a strong foundation in essential areas such as mathematical methods, big data technologies, business intelligence, machine learning and deep learning. The following courses are required of any student pursuing the MS in Data Analytics.

DATA 220 - Mathematical Methods for Data Analytics
- Advanced mathematical and statistical methods for data analytics, machine learning and time series analysis; selected topics from probability, statistics, linear algebra and mathematical optimization; programming for numerical implementation of mathematical and statistical algorithms.
  
  Prerequisites: Admission into MS in Data Analytics and graduate advisor consent.
DATA 228 - Big Data Technologies and Applications
- Leverage emerging big data systems, technologies, and applications to transform data into knowledge through capturing, managing, analyzing and understanding large data at volumes and rates; design analytical solutions in data warehouses, big data databases, data streaming, security and governance.
  
  Prerequisite: DATA 230
DATA 230 - Business Intelligence and Data Visualization
- Learn data visualization as an analytical tool and interactive dashboards; apply theories and design principles in the development of business intelligence and visualizations; and create data dashboards using BI tools, software packages and environments.
  
  Prerequisites: Admission into MS in Data Analytics and graduate advisor consent.
DATA 245 - Machine Learning Technologies
- Learn concepts, techniques and algorithms in machine learning for the development of real-world applications; build, evaluate and refine machine learning models to predict patterns from trained data sets; apply supervised and unsupervised learning methodologies in solving classification and regression problems.
  
  Prerequisite: DATA 220
  
  This course satisfies graduate-level GWAR in this master’s program. This course does NOT satisfy the undergraduate Writing in the Disciplines (WID) requirement.
DATA 255 - Deep Learning Technologies
- Study deep learning theory, techniques and technologies; implement neural networks using deep learning frameworks to solve problems in various application domains; build convolutional networks for image recognition, recurrent networks for sequence generation and generative adversarial networks for image generation.
  
  Prerequisites: DATA 229 and DATA 245
  Corequisite: DATA 265 or DATA 266

Elective Courses by Specialization (3 courses, 9 units)

The MS in Data Analytics offers two specializations, each requiring three courses in addition to the core courses and the culminating experience: the Analytics Technologies Specialization and the Data Engineering Specialization. For a full list of electives, see the All Elective Courses below.

Analytics Technologies Specialization

The Analytics Technologies Specialization offers a focused curriculum that equips students with cutting-edge skills in database systems, data mining and the application of large language models. By mastering these advanced technologies, graduates are well-prepared to tackle complex data challenges and drive innovation in a variety of professional settings.

DATA 225 - Database Systems for Analytics
- Learn data visualization as an analytical tool and interactive dashboards; apply theories and design principles in the development of business intelligence and visualizations; and create data dashboards using BI tools, software packages and environments.
  
  Prerequisites: Admission into MS in Data Analytics and graduate advisor consent.
DATA 240 - Data Mining and Analytics
- Learn concepts, tools, and algorithms for data mining, statistical modeling, implementation techniques, and applications of data mining with a focus on data mining functions: pattern discovery, cluster analysis, exploratory data analysis and anomaly detection with real-world examples.
  
  Prerequisite: DATA 225
DATA 265 - Large Language Model Applications
- Learn fundamentals of language models, application of large language models, entity recognition, including the use of prompt engineering, LangChain, advanced features of ChatGPT and fine tuning of Large Language models and their applications.
  
  Prerequisites: DATA 240 and DATA 245
  Corequisites: DATA 255

Data Engineering Specialization

The Data Engineering Specialization was developed to meet the growing demand for data engineering expertise among local companies; more than 150 Silicon Valley companies having hired our graduates. This specialization equips students with the advanced skills needed to design, build and manage sophisticated data systems that drive modern businesses.

DATA 226 - Data Warehouse and Pipeline
- Design analytical databases with dimensional and NoSQL data models; deploy data analytics applications with SQL programming; perform data cleansing, extract, transform and load (ETL) operations for data warehouses; build data pipelines; and perform online analytical processing (OLAP).
  
  Prerequisites: Admission into MSDA program and graduate advisor consent.
DATA 236 - Distributed Systems for Data Engineering
- Learn big data distributed systems, large scale distributed systems framework, distributed messaging framework, distributed system building with cloud technologies and REST (Representational State Transfer) service development and deployment.
  
  Prerequisite: DATA 226
DATA 266 - Generative Model Applications
- Learn application of generative models, including auto encoders, generative adversarial network, latent and stable diffusion, large language models and generative model applications, including the use of prompt engineering and advanced features of ChatGPT.
  
  Prerequisites: DATA 236 and DATA 245
  Corequisite: DATA 255

Culminating Experience Courses (2 courses, 6 units)

Complete either Plan A (Thesis) or Plan B (Project).

Plan A (Thesis)

Students who choose to complete a master’s thesis must enroll in DATA 299A and DATA 299B as part of a two-course sequence. It is the student’s responsibility to find a full-time tenured, or tenure-track, faculty member willing to serve as the chair of their thesis committee. Additionally, the student must secure the participation of at least two other university faculty members for the committee, one of whom must be a full-time, tenured professor. The student must write a thesis proposal and have it approved by the thesis committee and pass the DATA 299A course before enrolling in DATA 299B. The thesis must meet university requirements as stipulated in the SJSU catalog and in the SJSU Master’s Thesis and Doctoral Dissertation Guidelines. It will be written under the guidance of the candidate’s thesis committee chair with the assistance of the thesis committee.

DATA 299A MSDA Thesis I
- First of a two-course sequence for the Plan A (Thesis) culminating experience.
  
  Prerequisites: Classified status, good standing, DATA 228, DATA 245, satisfied GWAR, completed 12 units of MSDA courses and graduate advisor consent.
DATA 299B MSDA Thesis II
- Second of the two-course sequence for the Plan A (Thesis) culminating experience.
  
  Prerequisites: Advancement to candidacy, DATA 299A and graduate advisor consent.

Plan B (Project)

The graduate project is a research or development effort performed by a team of students on a topic chosen by mutual agreement between an advisor and the team. Both DATA 298A and DATA 298B are required. The choice of project topic must also be approved by the instructor of DATA 298.

DATA 298A MSDA Project I
- Comprehensive plan and preliminary design of data analytics project; integration of knowledge in technologies, processes and management to support program outcomes.
  
  Prerequisites: Classified status, good standing, DATA 228, DATA 245, satisfied GWAR, completed 12 units of MSDA courses and graduate advisor consent.
DATA 298B MSDA Project II
- Completion of an in-depth project to achieve the program outcomes and satisfy the culminating experience; write a detailed project report; make a comprehensive presentation and demonstration.
  
  Prerequisite(s): Advancement to candidacy, DATA 298A and graduate advisor consent.

Optional: Data Analytics Internship (1 course, 1 unit)

DATA 297 Data Analytics Internship

Fieldwork for MS in Data Analytics students. A report is required at the end of the term addressing the goals set at the start of the assignment.

Not required toward the degree; repeatable for Curricular Practical Training
Prerequisites: Completed 12 units of degree required courses, classified status, in good standing and graduate advisor consent

Data Analytics: All Elective Courses

Complete three or more courses to meet elective degree requirements (9 elective credits). See specializations above for specific courses required for the Analytics Technologies Specialization or the Data Engineering Specialization.

DATA 225 - Database Systems for Analytics
- Learn data visualization as an analytical tool and interactive dashboards; apply theories and design principles in the development of business intelligence and visualizations; and create data dashboards using BI tools, software packages and environments.
  
  Prerequisites: Admission into MS in Data Analytics and graduate advisor consent.
DATA 226 - Data Warehouse and Pipeline
- Design analytical databases with dimensional and NoSQL data models; deploy data analytics applications with SQL programming; perform data cleansing, extract, transform and load (ETL) operations for data warehouses; build data pipelines; and perform online analytical processing (OLAP).
  
  Prerequisites: Admission into MSDA program and graduate advisor consent.
DATA 236 - Distributed Systems for Data Engineering
- Learn big data distributed systems, large scale distributed systems framework, distributed messaging framework, distributed system building with cloud technologies and REST (Representational State Transfer) service development and deployment.
  
  Prerequisites: DATA 226
DATA 240 - Data Mining and Analytics
- Learn concepts, tools, and algorithms for data mining, statistical modeling, implementation techniques, and applications of data mining with a focus on data mining functions: pattern discovery, cluster analysis, exploratory data analysis and anomaly detection with real-world examples.
  
  Prerequisite: DATA 225
DATA 260 - Applied Intelligent Systems
- Apply modern computational intelligent techniques, methodologies, tools and environments to develop integrated systems for solving practical problems; incorporate development processes, architectures, smart components, models and algorithms in building intelligent systems; perform integration, operations, management and evaluation tasks of intelligent systems.
  
  Prerequisite: DATA 245
DATA 265 - Large Language Model Applications
- Learn fundamentals of language models, application of large language models, entity recognition, including the use of prompt engineering, LangChain, advanced features of ChatGPT and fine tuning of Large Language models and their applications.
  
  Prerequisites: DATA 240 and DATA 245
  Corequisite: DATA 255
DATA 266 - Generative Model Applications
- Learn application of generative models, including auto encoders, generative adversarial network, latent and stable diffusion, large language models and generative model applications, including the use of prompt engineering and advanced features of ChatGPT.
  
  Prerequisites: DATA 236 and DATA 245
  Corequisite: DATA 255
DATA 292 - Special Topics in Data Analytics
- Content varies from semester to semester. Repeatable in different topic areas.
  
  Course may be repeated for credit for up to 9 units.
  
  Prerequisite: Graduate advisor consent.
DATA 294 - Data Analytics Seminar
- Advanced and domain specific topics in the area of data analytics; content is updated in each offering; possible topics include though not restricted to research design and data analytics for the applications of business, engineering, natural science, social science, etc.
  
  Prerequisites: Graduate standing and instructor consent.
DATA 295 - Special Problems in Data Analytics
- Advanced individual work in data analytics. Repeatable in different problem areas.
  
  Course is repeatable for credit up to 6 units.
  
  Prerequisite: Graduate advisor consent

What is the current Program of Study?

The Program of Study (POS) is a plan of courses to take in sequence in order to satisfy the course requirements of the MSDA degree. After consulting with the program advisor, a full-time student should try their best to adhere to the following POS in order to complete the course requirements in time for graduation:

Semester 1: DATA 220, 226, 230, 200*, 201*, 202*
Semester 2: DATA 228, 245, 236
Semester 3: DATA 255, 266, 298A
Semester 4: DATA 298B

* These are remedial courses.